302 related articles for article (PubMed ID: 33006740)
1. Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes.
Lipsitz SR; Fitzmaurice GM; Weiss RD
Psychometrika; 2020 Dec; 85(4):890-904. PubMed ID: 33006740
[TBL] [Abstract][Full Text] [Related]
2. GEE with Gaussian estimation of the correlations when data are incomplete.
Lipsitz SR; Molenberghs G; Fitzmaurice GM; Ibrahim J
Biometrics; 2000 Jun; 56(2):528-36. PubMed ID: 10877313
[TBL] [Abstract][Full Text] [Related]
3. A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2017 Jul; 17(1):114. PubMed ID: 28743256
[TBL] [Abstract][Full Text] [Related]
4. Imputation strategies for missing binary outcomes in cluster randomized trials.
Ma J; Akhtar-Danesh N; Dolovich L; Thabane L;
BMC Med Res Methodol; 2011 Feb; 11():18. PubMed ID: 21324148
[TBL] [Abstract][Full Text] [Related]
5. Multiple imputation for handling missing outcome data when estimating the relative risk.
Sullivan TR; Lee KJ; Ryan P; Salter AB
BMC Med Res Methodol; 2017 Sep; 17(1):134. PubMed ID: 28877666
[TBL] [Abstract][Full Text] [Related]
6. Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: An application to AIDS data.
Lipsitz SR; Fitzmaurice GM; Ibrahim JG; Sinha D; Parzen M; Lipshultz S
J R Stat Soc Ser A Stat Soc; 2009 Jan; 172(1):3-20. PubMed ID: 20585409
[TBL] [Abstract][Full Text] [Related]
7. Multiple imputation for non-monotone missing not at random data using the no self-censoring model.
Ren B; Lipsitz SR; Weiss RD; Fitzmaurice GM
Stat Methods Med Res; 2023 Oct; 32(10):1973-1993. PubMed ID: 37647237
[TBL] [Abstract][Full Text] [Related]
8. Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2019 Jan; 19(1):14. PubMed ID: 30630434
[TBL] [Abstract][Full Text] [Related]
9. A comparison of multiple imputation methods for missing data in longitudinal studies.
Huque MH; Carlin JB; Simpson JA; Lee KJ
BMC Med Res Methodol; 2018 Dec; 18(1):168. PubMed ID: 30541455
[TBL] [Abstract][Full Text] [Related]
10. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.
Preisser JS; Lohman KK; Rathouz PJ
Stat Med; 2002 Oct; 21(20):3035-54. PubMed ID: 12369080
[TBL] [Abstract][Full Text] [Related]
11. The impact of dichotomization in longitudinal data analysis: a simulation study.
Yoo B
Pharm Stat; 2010; 9(4):298-312. PubMed ID: 19904810
[TBL] [Abstract][Full Text] [Related]
12. Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.
Touloumi G; Babiker AG; Pocock SJ; Darbyshire JH
Stat Med; 2001 Dec; 20(24):3715-28. PubMed ID: 11782028
[TBL] [Abstract][Full Text] [Related]
13. Analyzing longitudinal binary data in clinical studies.
Li Y; Feng D; Sui Y; Li H; Song Y; Zhan T; Cicconetti G; Jin M; Wang H; Chan I; Wang X
Contemp Clin Trials; 2022 Apr; 115():106717. PubMed ID: 35240309
[TBL] [Abstract][Full Text] [Related]
14. Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables.
Cao Y; Allore H; Vander Wyk B; Gutman R
Stat Med; 2022 Dec; 41(30):5844-5876. PubMed ID: 36220138
[TBL] [Abstract][Full Text] [Related]
15. Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings.
Donneau AF; Mauer M; Lambert P; Molenberghs G; Albert A
J Biopharm Stat; 2015; 25(3):570-601. PubMed ID: 24905056
[TBL] [Abstract][Full Text] [Related]
16. Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness.
Turner EL; Yao L; Li F; Prague M
Stat Methods Med Res; 2020 May; 29(5):1338-1353. PubMed ID: 31293199
[TBL] [Abstract][Full Text] [Related]
17. Approaches for missing covariate data in logistic regression with MNAR sensitivity analyses.
Ward RC; Axon RN; Gebregziabher M
Biom J; 2020 Jul; 62(4):1025-1037. PubMed ID: 31957905
[TBL] [Abstract][Full Text] [Related]
18. Bias in estimating association parameters for longitudinal binary responses with drop-outs.
Fitzmaurice GM; Lipsitz SR; Molenberghs G; Ibrahim JG
Biometrics; 2001 Mar; 57(1):15-21. PubMed ID: 11252590
[TBL] [Abstract][Full Text] [Related]
19. Multiple imputation in the presence of an incomplete binary variable created from an underlying continuous variable.
Grobler AC; Lee K
Biom J; 2020 Mar; 62(2):467-478. PubMed ID: 31304611
[TBL] [Abstract][Full Text] [Related]
20. Doubly robust and multiple-imputation-based generalized estimating equations.
Birhanu T; Molenberghs G; Sotto C; Kenward MG
J Biopharm Stat; 2011 Mar; 21(2):202-25. PubMed ID: 21390997
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]